R base functions for mass_dataset
Xiaotao Shen
Created on 2021-12-04 and updated on 2026-03-02
Source:vignettes/base_function.Rmd
base_function.Rmdmass_dataset object support many R base functions.
library(massdataset)
library(tidyverse)
data("expression_data")
data("sample_info")
data("sample_info_note")
data("variable_info")
data("variable_info_note")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info,
sample_info_note = sample_info_note,
variable_info_note = variable_info_note
)For example, you can get the information of your object.
dim(object)
#> variables samples
#> 1000 8
nrow(object)
#> variables
#> 1000
ncol(object)
#> samples
#> 8
dimnames(object)This means that object has 1000 variables and 8
samples.
apply(object, 2, mean)You can also get the sample ids and variables.
colnames(object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
head(rownames(object))
#> [1] "M136T55_2_POS" "M79T35_POS" "M307T548_POS" "M183T224_POS"
#> [5] "M349T47_POS" "M182T828_POS"Use [ to select variables and samples from object.
##only remain first 5 variables
object[1:5,]
#> --------------------
#> massdataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 5 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 8 samples:Blank_3 Blank_4 QC_1 ... PS4P3 PS4P4
#> 3.variable_info:[ 5 x 3 data.frame]
#> 5 variables:M136T55_2_POS M79T35_POS M307T548_POS M183T224_POS M349T47_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:35
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2026-03-02 09:27:35
##only remain first 5 samples
object[,1:5]
#> --------------------
#> massdataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 1000 x 5 data.frame]
#> 2.sample_info:[ 5 x 4 data.frame]
#> 5 samples:Blank_3 Blank_4 QC_1 QC_2 PS4P1
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:35
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2026-03-02 09:27:35
##only remain first 5 samples and 5 variables
object[1:5,1:5]
#> --------------------
#> massdataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 5 x 5 data.frame]
#> 2.sample_info:[ 5 x 4 data.frame]
#> 5 samples:Blank_3 Blank_4 QC_1 QC_2 PS4P1
#> 3.variable_info:[ 5 x 3 data.frame]
#> 5 variables:M136T55_2_POS M79T35_POS M307T548_POS M183T224_POS M349T47_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:35
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2026-03-02 09:27:35If you know the variables or sample names you want to select, you can also use the samples ids or variables ids.
colnames(object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
object[,c("Blank_3", "Blank_4")]
#> --------------------
#> massdataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 1000 x 2 data.frame]
#> 2.sample_info:[ 2 x 4 data.frame]
#> 2 samples:Blank_3 Blank_4
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 1000 variables:M136T55_2_POS M79T35_POS M307T548_POS ... M232T937_POS M301T277_POS
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2026-03-02 09:27:35
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2026-03-02 09:27:35
###log
object2 =
log(object + 1, 10)
unlist(object[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> NA NA 1857925 1037764 1494436 3496912 1959179 1005419
unlist(object2[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> NA NA 6.269028 6.016099 6.174478 6.543685 6.292074 6.002347
###scale
object2 =
scale(object, center = TRUE, scale = TRUE)
unlist(object[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> NA NA 1857925 1037764 1494436 3496912 1959179 1005419
unlist(object2[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2
#> NA NA 0.05372526 -0.83970979 -0.34223794 1.83914160
#> PS4P3 PS4P4
#> 0.16402547 -0.87494460Session information
sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.3
#>
#> Matrix products: default
#> BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
#>
#> time zone: Asia/Singapore
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1 purrr_1.1.0
#> [5] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
#> [9] magrittr_2.0.3 dplyr_1.1.4 ggplot2_4.0.2 massdataset_0.99.1
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 farver_2.1.2
#> [3] S7_0.2.0 fastmap_1.2.0
#> [5] digest_0.6.37 timechange_0.3.0
#> [7] lifecycle_1.0.4 cluster_2.1.8.1
#> [9] compiler_4.5.2 rlang_1.1.6
#> [11] sass_0.4.10 tools_4.5.2
#> [13] yaml_2.3.10 knitr_1.50
#> [15] S4Arrays_1.8.1 htmlwidgets_1.6.4
#> [17] DelayedArray_0.34.1 RColorBrewer_1.1-3
#> [19] abind_1.4-8 withr_3.0.2
#> [21] BiocGenerics_0.54.0 desc_1.4.3
#> [23] grid_4.5.2 stats4_4.5.2
#> [25] colorspace_2.1-1 scales_1.4.0
#> [27] iterators_1.0.14 dichromat_2.0-0.1
#> [29] SummarizedExperiment_1.38.1 cli_3.6.5
#> [31] rmarkdown_2.29 crayon_1.5.3
#> [33] ragg_1.4.0 generics_0.1.4
#> [35] rstudioapi_0.17.1 httr_1.4.7
#> [37] tzdb_0.5.0 rjson_0.2.23
#> [39] cachem_1.1.0 parallel_4.5.2
#> [41] XVector_0.48.0 matrixStats_1.5.0
#> [43] vctrs_0.6.5 Matrix_1.7-4
#> [45] jsonlite_2.0.0 IRanges_2.42.0
#> [47] hms_1.1.3 GetoptLong_1.0.5
#> [49] S4Vectors_0.48.0 clue_0.3-66
#> [51] systemfonts_1.2.3 foreach_1.5.2
#> [53] jquerylib_0.1.4 glue_1.8.0
#> [55] pkgdown_2.1.3 codetools_0.2-20
#> [57] stringi_1.8.7 shape_1.4.6.1
#> [59] gtable_0.3.6 GenomeInfoDb_1.44.2
#> [61] GenomicRanges_1.60.0 UCSC.utils_1.4.0
#> [63] ComplexHeatmap_2.24.1 pillar_1.11.0
#> [65] htmltools_0.5.8.1 GenomeInfoDbData_1.2.14
#> [67] circlize_0.4.16 R6_2.6.1
#> [69] textshaping_1.0.1 doParallel_1.0.17
#> [71] evaluate_1.0.4 Biobase_2.68.0
#> [73] lattice_0.22-7 png_0.1-8
#> [75] openxlsx_4.2.8 bslib_0.9.0
#> [77] Rcpp_1.1.0 zip_2.3.3
#> [79] SparseArray_1.8.1 xfun_0.53
#> [81] fs_1.6.6 MatrixGenerics_1.20.0
#> [83] pkgconfig_2.0.3 GlobalOptions_0.1.2